Unveiling the Power of Intermediate Representations for Static Analysis: A Survey
- URL: http://arxiv.org/abs/2405.12841v1
- Date: Tue, 21 May 2024 14:46:55 GMT
- Title: Unveiling the Power of Intermediate Representations for Static Analysis: A Survey
- Authors: Bowen Zhang, Wei Chen, Hung-Chun Chiu, Charles Zhang,
- Abstract summary: Static analysis techniques enhance the security, performance, and reliability of programs.
Intermediate Representation (IR) of a target program as input to retrieve essential program information.
Modern static analysis framework should possess the ability to conduct diverse analyses on different languages.
- Score: 10.2999755815712
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Static analysis techniques enhance the security, performance, and reliability of programs by analyzing and portraiting program behaviors without the need for actual execution. In essence, static analysis takes the Intermediate Representation (IR) of a target program as input to retrieve essential program information and understand the program. However, there is a lack of systematic analysis on the benefit of IR for static analysis, besides serving as an information provider. In general, a modern static analysis framework should possess the ability to conduct diverse analyses on different languages, producing reliable results with minimal time consumption, and offering extensive customization options. In this survey, we systematically characterize these goals and review the potential solutions from the perspective of IR. It can serve as a manual for learners and practitioners in the static analysis field to better understand IR design. Meanwhile, numerous research opportunities are revealed for researchers.
Related papers
- Easing Maintenance of Academic Static Analyzers [0.0]
Mopsa is a static analysis platform that aims at being sound.
This article documents the tools and techniques we have come up with to simplify the maintenance of Mopsa since 2017.
arXiv Detail & Related papers (2024-07-17T11:29:21Z) - Efficacy of static analysis tools for software defect detection on open-source projects [0.0]
The study used popular analysis tools such as SonarQube, PMD, Checkstyle, and FindBugs to perform the comparison.
The study results show that SonarQube performs considerably well than all other tools in terms of its defect detection.
arXiv Detail & Related papers (2024-05-20T19:05:32Z) - Customizing Static Analysis using Codesearch [1.7205106391379021]
A commonly used language to describe a range of static analysis applications is Datalog.
We aim to make building custom static analysis tools much easier for developers, while at the same time providing a familiar framework for application security and static analysis experts.
Our approach introduces a language called StarLang, a variant of Datalog which only includes programs with a fast runtime.
arXiv Detail & Related papers (2024-04-19T09:50:02Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to model and analyze multi-step attacks on computer networks.
This paper introduces an analysis-driven framework for AG generation.
It enables real-time attack path analysis before the completion of the AG generation with a quantifiable statistical significance.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - Understanding metric-related pitfalls in image analysis validation [59.15220116166561]
This work provides the first comprehensive common point of access to information on pitfalls related to validation metrics in image analysis.
Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy.
arXiv Detail & Related papers (2023-02-03T14:57:40Z) - Smoothness Analysis for Probabilistic Programs with Application to
Optimised Variational Inference [13.836565669337057]
We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program.
We show how the analysis can be used to improve the pathwise gradient estimator.
arXiv Detail & Related papers (2022-08-22T18:18:32Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Information-Theoretic Odometry Learning [83.36195426897768]
We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation.
The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
arXiv Detail & Related papers (2022-03-11T02:37:35Z) - Automatic Gaze Analysis: A Survey of DeepLearning based Approaches [61.32686939754183]
Eye gaze analysis is an important research problem in the field of computer vision and Human-Computer Interaction.
There are several open questions including what are the important cues to interpret gaze direction in an unconstrained environment.
We review the progress across a range of gaze analysis tasks and applications to shed light on these fundamental questions.
arXiv Detail & Related papers (2021-08-12T00:30:39Z) - A Comprehensive Review of Computer-aided Whole-slide Image Analysis:
from Datasets to Feature Extraction, Segmentation, Classification, and
Detection Approaches [21.317219960860267]
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis.
This paper reviews the methods of WSI analysis based on machine learning.
arXiv Detail & Related papers (2021-02-21T08:30:48Z) - Estimating Structural Target Functions using Machine Learning and
Influence Functions [103.47897241856603]
We propose a new framework for statistical machine learning of target functions arising as identifiable functionals from statistical models.
This framework is problem- and model-agnostic and can be used to estimate a broad variety of target parameters of interest in applied statistics.
We put particular focus on so-called coarsening at random/doubly robust problems with partially unobserved information.
arXiv Detail & Related papers (2020-08-14T16:48:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.